24 December 2012. A common variant at 16p11.2 increases risk of psychosis, according to a report published online November 20 in Molecular Psychiatry. Led by Kari Stefansson of deCODE Genetics in Reykjavik, Iceland, the study combined cases of schizophrenia, bipolar disorder, and other psychoses to search for variants related to psychosis, rather than to a specific diagnosis. The results flag a single nucleotide polymorphism (SNP) within a 593 kb region that has been previously linked to schizophrenia and other disorders by rare copy number variants (CNVs).

The findings build on their previous schizophrenia GWAS, which identified 39 single nucleotide polymorphisms (SNPs) that, though not genomewide-significant hits, had promising p values (see SRF related news story). In the new study, the researchers surveyed these same SNPs in an enlarged dataset, consisting of 27,543 cases with psychosis and 88,696 controls. A SNP at 16p11.2 attained genomewide significance (p = 6.6 x 10-11), with an odds ratio of 1.08, and suggests that combining diagnoses does not necessarily dilute psychosis-related GWAS signals.

The SNP lies in a region already on the radar: duplications are associated with schizophrenia, bipolar disorder, autism, and microcephaly, whereas deletions are associated with autism, developmental delay, and macrocephaly (see SRF related news story). Though a recent zebrafish study identified the KCTD13 gene in this region as controlling head size (see SRF related news story), the new study did not find that the SNP altered this gene’s expression in human brain tissue. They did, however, report an association between the SNP and expression of MAPK3, a gene that modified zebrafish head size in combination with KCTD13.—Michele Solis.

The three companion papers published in Nature provide important new evidence for a role of the MHC complex and common variation across the genome in risk for schizophrenia. These studies have exploited the availability of comprehensive genotyping technologies, coupled with large cohorts of cases and controls, to identify candidate loci for disease susceptibility.

A notable feature of these papers is the clear willingness of each of the groups to share its data, and to provide overlapping presentations of each others’ results. The combination of datasets permitted the statistical significance of the MHC findings to emerge, thereby increasing confidence in results. The implication that immune processes may interact with genetic risk to influence schizophrenia risk is consistent with several lines of evidence, including our own small GWAS study (Lencz et al., 2007) implicating cytokine receptors in schizophrenia susceptibility.

Perhaps most intriguing is the finding from the International Schizophrenia Consortium demonstrating that a “score” test—combining information from many thousands of common variants—can reliably differentiate patients and controls across multiple psychiatric cohorts. These results indicate that hundreds, if not thousands, of genes of small effect may contribute to schizophrenia risk. Moreover, these same genes were shown to contribute to bipolar risk (but not risk for non-psychiatric disorders such as diabetes).

Much more work remains to be done in psychiatric genetics. While the score test accounted for about 3 percent of the observed case-control variance, statistical modeling suggested that common variation could explain as much as one-third or more of the total risk. Nevertheless, there remains a substantial proportion of genetic “dark matter” (unexplained variance), given the high heritability of a disorder such as schizophrenia. Complementary approaches are needed to further parse the source of the common genetic variance, as well as to identify rare yet highly penetrant mutations. Additional techniques, such as pharmacogenetic studies and endophenotypic research, will help to explicate the functionality and clinical significance of observed risk alleles.

The three Nature papers reporting GWAS results in a large sample of cases of schizophrenia and controls from around Western Europe and the U.S. are decidedly disappointing to those expecting this strategy to yield conclusive evidence of common variants predicting risk for schizophrenia. Why has this extensive and very costly effort not produced more impressive results? There are likely to be many explanations for this, involving the usual refrains about clinical and genetic heterogeneity, diagnostic imprecision, and technical limitations in the SNP chips. But the likely, more fundamental problem in psychiatric genetics involves the biologic complexity of the conditions themselves, which renders them especially poorly suited to the standard GWAS strategy. The GWA analytic model assumes fixed, predictable relationships between genetic risk and illness, but simple relationships between genetic risk and complex pathophysiological mechanisms are unlikely. Many biologic functions show non-linear relationships, and depending on the biologic context, more of a potential pathogenic factor, can make things worse or it can make them better. Studies of complex phenotypes in model systems illustrate that individual gene effects depend upon non-linear interactions with other genes (Toma et al., 2002; Shaoa et al, 2008). Similar observations are beginning to emerge in human disorders, e.g., in risk for cancer (Lo et al., 2008) and depression (Pezawas et al., 2008).

The GWA approach also assumes that diagnosis represents a unitary biological entity, but most clinical diagnoses are syndromal and biologically heterogeneous, and this is especially true in psychiatric disorders. Type 2 diabetes is the clinical expression of changes in multiple physiologic processes, including in pancreatic function, in adipose cell function, as well as in eating behavior. Likewise, hypertension results from abnormalities in many biologic processes (e.g., vascular reactivity, kidney function, CNS control of blood pressure, metabolic factors, sodium regulation), and even a large effect on any specific process within a subset of individuals will seem small when measured in large unrelated samples (Newton-Cheh et al., 2009). In the case of the cognitive and emotional problems associated with psychiatric disorders, the biologic pathways to clinical manifestations are probably much more heterogeneous. While the results of GWAS in disorders like type 2 diabetes and hypertension have been more informative than in the schizophrenia results so far, they, too, have been disappointing, considering all the fanfare about their expectations. But given the pathophysiologic realities of diabetes, hypertension, or psychiatric disorders, how could the effect of any common genetic variant acting on only one of the diverse pathophysiological mechanisms implicated in these disorders be anything other than small when measured in large pathophysiologically heterogeneous populations? Other approaches, e.g., family studies, studies of smaller but much better characterized samples, and studies of genetic interactions in these samples, will be necessary to understand the variable genetic architectures of such biologically complex and heterogeneous disorders.

The synthesis and extraction of the essence of the 3 Nature papers by Heimer and Farley represents science reporting at its best. Completion of the task while the ink was still wet shows that SRF is indeed in good hands. Congratulations on being concise, even-handed, non-judgmental, and challenging under the pressure of time.

Schizophrenia Genetics: Glass Half Full?
While it may be disappointing that the GWAS described above did not identify more genes, they nevertheless represent a landmark in psychiatric genetics and suggest a dual approach for the future: continued large-scale genetic association studies along with alternative genetic approaches leading to the discovery of new genetic etiologies, and more functional investigations to identify pathways of pathogenesis—which may themselves suggest new etiologies.

The consistent identification of an association with the MHC locus reinforces (without proving, as pointed out in the SRF news story) long-standing interest in the involvement of infectious or immune factors in schizophrenia pathogenesis (Yolken and Torrey, 2008). Epidemiologic and neuropathological studies that include patients selected for the presence or absence of immunologic genetic risk variants could potentially clarify etiology; cell and mouse model studies could clarify pathogenesis (Ayhan et al., 2009). It is striking that a major genetic finding in schizophrenia serves to reinforce the concept of environmental risk factors.

The two specific genes identified by the SGENE consortium, NRGN and TCF4, offer intriguing new leads into schizophrenia. This should foster a number of further genetic and neurobiological studies. Deep resequencing (and CNV analysis) can detect rare causative mutations, as exemplified by TCF4 mutations leading to Pitt-Hopkins syndrome. Neurogranin already has clear connections to interesting signaling pathways related to glutamate transmission. A hope is that further studies of both gene products and their interactions will identify pathogenic pathways.

The ISC used common genetic variants “en masse” to generate a “polygene score” from discovery samples of patients; that score was able to predict case status in test populations. The success of this approach provides very strong evidence that a portion of schizophrenia risk status is attributable to common genetic variants acting in concert and that schizophrenia shares genetic factors with bipolar disorder, but not with other diseases. This analysis has multiple practical implications for the direction of research. First, since polygenic factors explain only a portion of the genetic risk, the search for other genetic factors—rare mutations of major effect detectable by deep sequencing, CNVs, variations in tandem repeats (Bruce et al., 2009, in press), and other genomic lesions—takes on new importance. Second, a meaningful integration of polygenic factors in a way that facilitates understanding of schizophrenia pathogenesis and the discovery of therapeutic targets will require identification of relevant pathways. Examination of patient-derived material—such as neurons differentiated from induced pluripotent stem cells taken from well-characterized, patient populations—may be of great value.

The remarkable overlap between the genetic factors of schizophrenia and bipolar disorder suggests the need for further and more inclusive clinical studies—not just of “endophenotypes,” but also of the phenotypes themselves, together, rather than in isolation (Potash and Bienvenu, 2009). For instance, it is only within the past few years that the importance of cognitive dysfunction in schizophrenia has been appreciated. Cognition in bipolar disorder is even less well studied.

How much is really known about the longitudinal course of both disorders? Do genetic factors predict disease outcome? It is only recently that studies have focused intensively on the early course of schizophrenia and its prodrome. Much more is still to be learned, and even less is known about bipolar disorder. In conjunction with this greater understanding of clinical phenotype, it will clearly be necessary to refine the approach to phenotype by establishing the biological framework for these diseases and by establishing biomarkers, such as disruption in white matter (Karlsgodt et al., 2009) or abnormalities in functional networks (Demirci et al., 2009), that cut across current nosological categories. In turn, longitudinal study of clinical, imaging, and functional outcomes of schizophrenia and bipolar disorders should facilitate both focused candidate genetic studies and GWAS of large populations.

References:

Yolken RH, Torrey EF. Are some cases of psychosis caused by microbial agents? A review of the evidence. Mol Psychiatry. 2008 May;13(5):470-9. Abstract

Comment by: David CollierSubmitted 6 July 2009
Posted 6 July 2009 I recommend the Primary Papers

This report is unnecessarily negative, from my point of view. The three studies show not only that GWAS can identify susceptibility alleles for schizophrenia, but that the majority of risk comes from common variants of small effect. These can be found, but as in other complex traits and diseases, such as obesity and height, considerable power is needed, because effect sizes are small, meaning greater samples sizes. This approach works: there are now almost 60 variants influencing height (Hirschhorn et al., 2009; Soranzo et al., 2009; Sovio et al., 2009). Furthermore, the genes identified so far from both traditional mapping, CNV analysis and GWAS, point to two biological pathways, the integrity of the synapse (neurexin 1, neurogranin, etc.) and the wnt/GSK3β signaling pathway (DISC1, TCF4, etc.), which is involved in functions such as neurogenesis in the brain. The identification of disease pathways for schizophrenia has major implications and should not be underestimated. It would be daft to lose nerve now and turn away from GWAS just as they are bearing fruit.

I would like to correct/expand on my comments to Peter Farley, to say that while statistical significance for some markers may be reached sooner, significance for many of the hundreds if not thousands of common schizophrenia susceptibility alleles of small effect might not emerge until samples of 100,000 cases and more than 100,000 controls have been collected. Another point is that organizations such the Wellcome Trust are already assembling case samples for schizophrenia as well as control samples.

Also, I would like to clarify that I believe the remainder of genetic variation, after common variation has been taken into account, will come from some combination of rare CNVs, other rare variants such as SNPs and other types of genetic marker such as variable number of tandem repeats (VNTRs) and of course the much neglected contribution from gene-environment interactions, in which main genetic effects may be obscured.

Some commentators in their reflections take a rather negative view on what has been achieved through the application of GWAS technology to schizophrenia and psychiatric disorders more generally. We strongly disagree with this position. Below, we give examples of a number of statements that can be made about the aetiology of schizophrenia and bipolar disorder that could not be made at high levels of confidence even two years ago that are based upon evidence deriving from the application of GWAS.

1. We know with confidence that the role of rare copy number variants in schizophrenia is not limited to 22q11DS (VCFS) (reviewed recently in O’Donovan et al., 2009). We do not yet know how much of a contribution, but we know the identity of an increasing number of these. Most span multiple genes so it may prove problematic as it has in 22q11DS to identify the relevant molecular mechanisms. However, for one locus, the CNVs are limited to a single gene: Neurexin1 (Kirov et al., 2008; Rujescu et al., 2009). Genetic findings are merely the start of the journey to a deeper biological understanding, but no doubt many neurobiological researchers have already embarked on that journey in respect of neurexin1.

2. Although we have argued in this forum that some of the major pre-GWAS findings in
schizophrenia very likely reflect true susceptibility genes (DTNBP1, NRG1, etc), we now have at
least 4 novel loci where the evidence is more definitive (ZNF804A, MHC, NRGN, TCF4),
(O’Donovan et al., 2008a; ISC, 2009; Shi et al., 2009; Stefansson et al., 2009) and two novel loci (Ferreira et al., 2008) in bipolar disorder (ANK3 and CACNA1C), at least one of which (CACNA1C) additionally confers risk of schizophrenia (Green et al., 2009). This is obviously a small part of the picture, but it is certainly better than no picture at all. These findings also offer a much more secure foundation than the earlier findings upon which to build follow up studies, for example brain imaging, and cognitive phenotypes (Esslinger et al., 2009), and even candidate gene studies. We would not regard the first convincing evidence that altered calcium channel function is a primary aetiological event in at least some forms of psychosis as a trivial gain in knowledge.

3. We can say with confidence that common alleles of small effect are abundant in
schizophrenia, and that they contribute to a substantial part of the population risk (ISC, 2009).
Identifying any one of these at stringent levels of statistical significance may be challenging in
terms of sample sizes. As we have pointed out before, merging multiple datasets may indeed obscure some true associations because of sometimes unpredictable relationships between risk alleles and those assayed indirectly in GWAS studies (Moskvina and O’Donovan, 2007). Nevertheless, that many of the same alleles are overrepresented in multiple independent GWAS datasets from different countries (ISC, 2009) means that larger samples offer the prospect of identifying many more of these. This is not to say that large samples are the only approach; genetic heterogeneity may well underpin some aspects of clinical heterogeneity (Craddock et al., 2009a). However, with the exception of individual large pedigrees, it is not yet evident which type of clinical sample one should base a small scale study on. It should also be self-evident that the analysis of multiple samples, each with a different phenotypic structure, will pose major problems in respect of multiple testing and subsequent replication. Moreover, ascertaining special samples that represent putative subtypes of the clinical (and endophenotypic) spectrum of psychosis will first require large samples to be carefully assessed and the relevant subjects extracted. Subsequently, downstream, evaluation of specific genotype-phenotype relationships will require the remainder of the clinical population to be genotyped in a suitably powered way to show that those effects are specific to some clinical features of the disorder. Increasingly, it is ascertainment and assessment that dominate the cost of GWAS studies so it is not clear this approach will achieve any economies. We must also remember that after a GWAS study, there remains the opportunity to look in a controlled manner for relatively specific associations in the context of the heterogeneous clinical picture. For example we are aware of a number of papers in development that will exploit the sorts of multi-locus tests reported by the ISC to refine diagnostics, and no doubt many other applications of this will emerge in the next year or so.

Critics should bear in mind that the GWAS data are not just there for the ‘headline’ genome-wide findings, but that the data will be available to mine for years to come. The findings reported to date are based on only the simplest analyses.

4. If it were the case that the thousands of SNPs of small effect were randomly distributed across biological systems, none being of more relevance to pathophysiology than another, identifying them would probably be a pointless endeavour. However, there is no reason to believe this will be the case. We have recently shown that in bipolar disorder, the GWAS signals are enriched in particular biological pathways (Holmans et al., 2009) and we also published strong evidence for a relatively selective involvement of the GABAergic system in schizoaffective disorder (Craddock et al., 2009b). We are aware of an as-yet unpublished independent sample with similar findings. We would not regard the first convincing evidence that altered GABA function is a primary aetiological event in at least some forms of psychosis as a trivial gain in knowledge.

Incidentally it is a common misconception that the identification of risk alleles of small effect necessarily confers no useful insights into pathogenesis and possible drug targets. For example, common alleles in PPARG and KCNJ11 have been robustly shown to confer risk to Type 2 diabetes (T2D) but with odds ratios in the region of only 1.14 (of similar magnitude to those revealed by GWAS of schizophrenia). PPARG encodes the target for the thiazolidinedione class of drugs used to treat T2D. KCNJ11 encodes part of the target for another class of diabetes drug, the sulphonylureas (Prokopenko et al., 2008). Moreover, studies of novel associated variants identified in T2D GWAS in healthy, non-diabetic, populations have demonstrated that for most, the primary effect on T2D susceptibility is mediated through deleterious effects on insulin secretion, rather than insulin action (Prokopenko et al., 2008). Further examples of insights into the biology of common diseases coming from the identification of loci of small effect are the implication of the complement system in age-related macular degeneration and autophagy in Crohn’s disease (Hirschhorn, 2009). Already, efforts are under way to translate the new recognition of the role of autophagy in Crohn’s disease into new therapeutic leads (Hirschhorn, 2009). Of course many of the loci identified in GWAS implicate genes whose functions are as yet largely or completely unknown, and determining those functions is a prerequisite of translating those findings. Nevertheless, we believe that the greatest benefits that will accrue from the continued discovery of risk loci through GWAS will come from the assembly of that information into novel disease pathways leading to novel therapeutic targets.

5. We can say with confidence that bipolar disorder and schizophrenia substantially overlap, at least in terms of polygenic risk (ISC, 2009). As clinicians, we do not regard that knowledge as a trivial achievement.

6. We can say with confidence from studies of CNVs that schizophrenia and autism share at least some risk factors in common (O’Donovan et al., 2009). We believe that is also an important insight.

The above are major achievements in what we expect to be a long but accelerating process of picking apart the origins of schizophrenia and other psychotic disorders. We do not think that any other research discipline in psychiatry has done more to advance that knowledge in the past 100 years.

Like that other common familial diseases, the genetics of schizophrenia and bipolar disorder is a “mixed economy” of common alleles of small effect and rare alleles of large and small effects, including CNVs. Those who are concerned at the cost of collecting large samples for GWAS studies must bear in mind that the robust identification of both types of mutation will require similarly large samples; we will just have to get used to that fact if we want to make progress. Collecting samples like this may be expensive, but as clinicians, we know those costs are trivial compared with the human and economic costs of psychotic disorders.

The question of phenotype definition is one which we have repeatedly addressed (Craddock et al., 2009a). Unquestionably, if we knew the true pathophysiological basis of these disorders, we could do better. The fact is that we don’t. Given that, it must be extremely encouraging that despite the problems, risk loci can be robustly identified by GWAS using samples defined by current diagnostic criteria. Moreover, armed with GWAS data in these heterogeneous populations, additional risk genes can be identified through strategies aimed at refining the phenotype that are not constrained by the current dichotomous view of the functional psychoses. We have shown at least one way in which this might be achieved without imposing a further burden of multiple testing (Craddock et al., 2009b), and have little doubt that others will emerge. We agree that approaches to phenotyping that more directly index underlying pathophysiology are highly appealing, and will ultimately be necessary for understanding the mechanistic relationships between gene and disorder. However, the two cardinal assumptions upon which the use of endophenotypes is predicated for gene discovery are questionable. First, there is little good evidence that putative endophenotypes are substantially simpler genetically than “exophenotypes” (Flint and Munafo, 2007). Second, there is rarely good evidence that the current crop of popular putative endophenotypes lie on the disease pathway, indeed there seems to be substantial pleiotropy in the genetics of complex traits, psychosis included (Prokopenko et al., 2008; O’Donovan et al., 2008b).

Finally, we reiterate that while only small parts of the heritability of any complex disorder have been accounted for, large-scale genetic approaches have been extremely successful in studies of non-psychiatric diseases (Manolio et al., 2008) and have led to substantial advances in our understanding of pathogenesis, even for diseases like Crohn’s disease where there was already prior knowledge of pathogenesis from other research methods (Mathew, 2008).

Psychiatry starts from a situation in which there is no robust prior knowledge of pathogenesis for the major phenotypes. Recent findings suggest that mental illness may be the medical field that will actually benefit most over the coming years from application of these powerful molecular genetic technologies.

Moskvina V and O'Donovan MC. (2007) Detailed analysis of the relative power of direct and indirect association studies and the implications for their interpretation. Human Heredity 64(1):63-73. Abstract

GWAS Results: Is the Glass Half Full or 95 Percent Empty?
The publication of the latest schizophrenia GWAS papers represents the culmination of a tremendous amount of work and unprecedented cooperation among a large number of researchers, for which they should be applauded. In addition to the hope of finding new “schizophrenia genes,” GWAS have been described by some of the researchers involved as, more fundamentally, a stern test of the common variants hypothesis. Based on the meagre haul of common variants dredged up by these three studies and their forerunners, this hypothesis should clearly now be resoundingly rejected—at least in the form that suggests that there is a large, but not enormous, number of such variants, which individually have modest, but not minuscule, effects. There are no common variants of even modest effect.

However, Purcell and colleagues now argue for a model involving vast numbers of variants, each of almost negligible effect alone. The authors show that an aggregate score derived from the top 10-50 percent of a set of 74,000 SNPs from the association results in a discovery sample can predict up to 3 percent of the variance in a target group. Simply put, a set of putative “risk alleles” can be defined in one sample and shown, collectively, to be very slightly (though highly significantly in a statistical sense) enriched in the test sample, compared to controls. This is consistent across several different schizophrenia samples and even in two bipolar disorder samples. The authors go on to perform a set of control analyses that suggest that these results are not due to obvious population stratification or genotype rate effects (although effects at this level are obviously prone to cryptic artifacts).

If taken at face value, what do these results mean? They imply some kind of polygenic effect on risk, but of what magnitude? The answer to that depends on the interpretation of the additional simulations performed by the authors. They argue that the risk allele set inevitably contains very many false positives, which dilute the predictive power of the real positives hidden among them. Based on this logic, if we only knew which were the real variants to look at, then the variance explained in the target group would be much greater.

To try and estimate the magnitude of the effect of the polygenic load of “true risk” alleles, the authors conducted a series of simulations, varying parameters such as allele frequencies, genotype relative risks, and linkage disequilibrium with genotyped markers. They claim that these analyses converge on a set of models that recapitulate the observed data and that all converge on a true level of variance explained of around 34 percent, demonstrating a large polygenic component to the genetic architecture of schizophrenia.

These simulations adopt a level of statistical abstraction that should induce a healthy level of skepticism or at least reserved judgment on their findings. Most fundamentally, they rely explicitly for their calculations of the true variance on a liability-threshold model of the genetic architecture of schizophrenia. In effect, the “test” of the model incorporates the assumption that the model is correct.

The liability-threshold model is an elegant statistical abstraction that allows the application of the powerful statistics of normal distributions. Unfortunately, it suffers from the fact that it has no support whatsoever and makes no biological sense. First, there is no justification for assuming a normal distribution of “underlying liability,” whatever that term is taken to mean. Second, as usual when it is invoked, the nature of this putative threshold is not explained, though it surreptitiously implies some form of very strong epistasis (to explain the difference in risk between someone with x liability alleles and someone else with x+1 alleles). If this model is not correct, then these simulations are fatally flawed.

Even if the model were correct, the calculations are far from convincing. From a starting set of 560 models, the authors arrive at seven that are consistent with the observed degree of prediction in the target samples. According to the authors, the fact that these seven models converge on a small range of values for the underlying variance explained by the markers is evidence that this value (around 34 percent) represents the true situation. What is not highlighted is the fact that the values for the actual additive genetic variance (taking into account incomplete linkage disequilibrium between the markers and the assumed causal variants) across these models ranges from 34 percent to 98 percent and that the number of SNPs assumed to be having an effect ranges from 4,625 to 74,062. This extreme variation in the derived models hardly inspires confidence in the authors’ claim that their data “strongly support a polygenic basis to schizophrenia that (1) involves common SNPs, [and] (2) explains at least one-third of the total variation in liability.” (italics added)

From a more theoretical perspective, it should be noted that a polygenic model involving thousands of common variants of tiny effect cannot explain and will not contribute to the observed heightened familial relative risks. Such risk can only be explained by a variant of large effect or by an oligogenic model involving at most two to three loci (Bodmer and Bonilla, 2008; Hemminki et al., 2008; Mitchell and Porteous, in preparation). It seems much more likely that the observed predictive power in the target samples represents a modest “genetic background” effect, which could influence the penetrance and expressivity of rare, causal mutations. However, if the point of GWAS is to find genetic variants that are predictive of risk or that shed light on the pathogenic mechanisms of the disease, then clearly, even if such variants can be found by massively increasing sample sizes, their identification alone would not achieve or even appreciably contribute to either of these goals.

Thumbs up or down on schizophrenia GWAS?
The triumvirate of schizophrenia GWAS studies just published in Nature gives cause for thought, and bears close scrutiny and reflection. To my reading, these three studies individually and collectively lead to an unambiguous conclusion—there is a lot of genetic heterogeneity and not one individual variant of common ancient origin accounts for a significant fraction of the genetic liability. To put it another way, there is no ApoE equivalent for schizophrenia. Strong past claims for ZNF804A and others look to have fallen by the statistical wayside. Putting the results of all three studies together does appear to provide support for a long known, pre-GWAS association with HLA, but otherwise it is hard to give a strong "thumbs up" to any specific result, not least because of the lack of replication between studies. The results are nevertheless important because the common disease, common variant model, on which GWAS are based and the associated cost justified, is strongly rejected as the main contributor to the genetic variance.

The ISC proposes a highly polygenic model with thousands of variants having an additive effect on both schizophrenia and bipolar disorder. I find no fault with their evidence, but its meaning and interpretation remains speculative. Simply consider the fact that SNPs carefully selected to tag half the genome account for about a third of the variance. It follows that the lion's share has gone undetected and will, by design and limitation, remain impervious to the GWAS strategy.

Part of the GWAS appeal is that the genotyping is technically facile and it is easier to collect lots of cases than it is families, but for as long as a diagnosis of schizophrenia or BP depends upon DSM-IV or ICD-10 classification, then diagnostic uncertainty will have a major effect on true power and validity of statistical association, both positive or negative. Indeed, the longstanding evidence from variable psychopathology amongst related individuals, the recent epidemiology evidence for shared genetic risk for schizophrenia and BP, and the further evidence supporting this from the ISC GWAS, all suggest that we should be returning more to family-based studies as a strategy to reduce genetic heterogeneity and find explanatory genetic variants. Plainly, adding ever more uncertainty through ever larger sample sizes is neither smart nor efficient.

I would certainly give the thumbs up to the full and unencumbered release of the primary data to the community as a whole, as this could usefully recoup some of the GWAS investment. It would facilitate a range of statistical and bioinformatics analyses and, who knows, there might be hidden nuggets of statistical support for independent genetic and biological studies.

The main question that arises from the three large genomewide association studies published in Nature is, What should we do next?

One important way forward would be to follow up the association findings in the MHC region. We need to understand the biological mechanism underlying this association. If the association signal is indeed related to infectious diseases, this line of inquiry may lead to the highly desired development of a treatment that might prevent the diseases in some cases.

One possible explanation for the association between schizophrenia and the MHC region (6p22.1) is that infection during pregnancy leads to disturbances of fetal brain development and increases the risk of schizophrenia later in life. A possible test for the theory of infectious diseases as risk factors for schizophrenia would be to study the associated SNPs in 6p22.1 in fathers and mothers of subjects with schizophrenia relative to parents of control subjects. If the 6p22.11 region is related to the tendency of mothers to be infected by viruses during pregnancy, we would expect the SNPs in 6p22.1 to be most strongly associated with being a mother to a subject with schizophrenia.

Another broader and more complicated part of the question is: What would be the best strategy for continued study of the genetic causes of schizophrenia? There shouldn’t be only one way to proceed. Testing samples that are 10 times larger seems likely to lead to the identification of more genes, but with much smaller effect size. Testing the association of common variants with schizophrenia is unlikely to lead to the development of genetic diagnostic tools in the near future. If we want to understand the biology of the disease, it might be easier to concentrate our efforts on the identification of rare inherited and non-inherited variants with large effect on the phenotype. Such rare variants are easier to model in animals (relative to common variants with very small functional effect) and might even account for a larger proportion of cases.

The three companion papers in this week’s issue of Nature, in our view, support the case for investigating interaction between susceptibility genes and infectious exposures in schizophrenia. We and others have argued previously that genetic studies conducted in isolation from environmental factors, and studies of environmental influences in the absence of genetic data, are necessarily limited. Maternal influenza, rubella, toxoplasmosis, herpes simplex virus, and other infections have each been associated with an increased risk of schizophrenia, with effect sizes ranging from twofold to over fivefold. While these epidemiologic findings clearly require replication in independent cohorts, two new developments provide further support for the hypothesis. First, a growing number of animal studies of maternal immune activation have documented behavioral and brain phenotypes in offspring that are analogous to findings from clinical research in schizophrenia, and these findings are mediated in large part by specific cytokines (Meyer et al., 2009; Patterson, 2008). Second, recent evidence indicates that maternal infection is also related to deficits in executive and other cognitive functions and neuropathology thought to arise from disruptions in brain development (Brown et al., 2009a; Brown et al., 2009b).

While the MHC region contains genes not involved in the immune system, in light of the epidemiologic findings on maternal infection, it is intriguing to see that this region is once more implicated in genetic studies of schizophrenia as the importance of this region in the response to infectious insults cannot be ignored. Although it is heartening to see that the potential implications of these findings for infectious etiologies were raised in the article from the SGENE plus group, an analysis of the frequency of SNPs by season of birth falls well short of the type of research that will yield definitive findings on the relationships between susceptibility genes and infectious insults. Hence, we advocate a strategy aimed at large scale genetic analyses of schizophrenia cases using birth cohorts with infectious exposures documented from prospectively collected biological samples from the prenatal period. If the schizophrenia-related pathogenic mechanisms by which MHC-related genetic variants operate involve interactions with prenatal infection, we would expect that studies of gene-infection interaction will yield larger effect sizes than those found in these new papers. The evidence from these papers and the epidemiologic literature should also facilitate narrowing of the number of candidate genes to be tested for interactions with infectious insults, thereby ameliorating the potential for type I error due to multiple comparisons.

Two hundred years after Darwin’s birth and 150 years after the publication of On the Origin of Species, these three papers in Nature show the important role of natural selection in shaping the genetic architecture of schizophrenia susceptibility. If we compare the GWAS results for schizophrenia with those obtained for other diseases, it seems that there are less common risk alleles and/or lower effect sizes in schizophrenia than in many other complex diseases (see, for instance, the online catalog of published GWAS at NHGRI). This fact strongly suggests that negative selection limits the spread of susceptibility alleles, as expected due to the decreased fertility of schizophrenic patients.

Interestingly, the MHC region may be an exception. This region represents a classical example of balancing selection, i.e., the presence of several variants at a locus maintained in a population by positive natural selection (Hughes and Nei, 1988). In the case of the MHC, this balancing selection seems to be related to pathogen resistance or MHC-dependent mating choice. Therefore, the presence of common schizophrenia susceptibility alleles at this locus might be explained by antagonistic pleiotropic effects of alleles maintained by natural selection.

If negative selection limits the spread of schizophrenia risk alleles, most of the genetic susceptibility to schizophrenia is likely due to rare variants. Resequencing technologies will allow the identification of many of these variants in the near future. In the meantime, it would be interesting to focus our attention on non-synonymous SNPs at low frequency. Based on human-chimpanzee comparisons and human sequencing data, Kryukov et al. (2008) have shown that a large fraction of de novo missense mutations are mildly deleterious (i.e., they are subject to weak negative selection) and therefore they can still reach detectable frequencies. Assuming that most of these mildly deleterious alleles may be detrimental (i.e., they confer risk for disease) the authors conclude that numerous rare functional SNPs may be major contributors to susceptibility to common diseases Kryukov et al., 2008. Similar conclusions were obtained by the analysis of the relative frequency distribution of non-synonymous SNPs depending on their probability to alter protein function (Barreiro et al., 2008; Gorlov et al., 2008). As shown by Evans et al. (2008), genomewide scans of non-synonymous SNPs might complement GWAS, being able to identify rare non-synonymous variants of intermediate penetrance not detectable by current GWAS panels.

One Hundred Years of Insanity: The Relationship Between Schizophrenia and Autism
The great Colombian author Gabriel García Márquez reified the cyclical nature of history in his Nobel Prize-winning 1967 book, One Hundred Years of Solitude. Eugen Bleuler’s less-famous book Dementia Præcox or the Group of Schizophrenias, originally published in 1911, saw first use of the term “autism,” a form of solitude manifest as withdrawal from reality in schizophrenia. This neologism, about to celebrate its centenary, epitomizes an astonishing cycle of reification and change in nosology, a cycle only now coming into clear view as molecular-genetic data confront the traditional, age-old categories of psychiatric classification.

The term autism was, of course, redefined by Leo Kanner (1943) for a childhood psychiatric condition first considered as a subset of schizophrenia, then regarded as quite distinct (Rutter, 1972) or even opposite to it (Rimland, 1964; Crespi and Badcock, 2008), and most recently seen by some researchers as returning to its original Bluelerian incarnation (e.g., Carroll and Owen, 2009). An outstanding new paper by McCarthy et al. (2009), demonstrating that duplications of the CNV locus 16p11.2 are strongly associated with increased risk of schizophrenia, has brought this question to the forefront of psychiatric inquiry, because deletions of this same CNV are one of the most striking recently-characterized risk factors for autism. Additional CNVs, such as those at 1q21.1 and 22q11.21 have also been associated with autism and schizophrenia in one or more studies (e.g., Mefford et al., 2008; Crespi et al., 2009; Glessner et al., 2009), which has led some authors to infer that since an overlapping set of loci mediates risk of both conditions, autism and schizophrenia must be more similar than previously conceived (e.g., Carroll and Owen, 2009; Guilmatre et al., 2009). Similar considerations apply to several genes, such as CNTNAP2 and NRXN1, various disruptions of which have likewise been linked with both conditions (Iossifov et al., 2008; Kirov et al., 2008; Burbach and van der Zwaag, 2009).

So does this plethora of new molecular-genetic data imply that Blueler was indeed correct, if not prescient, that autism and schizophrenia are manifestations of similar disease processes? The answer may appear tantalizingly close, but will likely remain inaccessible without explicit consideration of alternative hypotheses and targeted tests of their differentiating predictions. This approach is simply Platt’s (1964) classic method of strong inference, which has propelled molecular biology so far and fast but left psychiatry largely by the wayside (Cannon, 2009). The alternative hypotheses in this case are clear: with regard to causation from specific genetic and genomic risk factors, autism and schizophrenia are either: 1) independent and discrete, 2) partially yet broadly overlapping, 3) subsumed with autism as a subset of schizophrenia, or 4) diametrically opposite, with normality in the centre. CNVs are especially useful for testing among such alternative hypotheses, because they naturally involve highly-penetrant perturbations in two opposite directions, due to deletions vs duplications of more or less the same genomic regions. Hypotheses 2), 3) and 4) thus predict that autism and schizophrenia should share CNV risk loci, but 2) and 3) predict specific rearrangements (deletions, duplications, or both) shared across both conditions; by contrast, hypothesis (4) predicts that, as highlighted by McCarthy et al. (2009), reciprocal CNVs at the same locus should mediate risk of autism versus schizophrenia. This general approach was pioneered by Craddock et al. (2005, 2009), in their discussion of explicit alternative hypotheses for the relationship between schizophrenia and bipolar disorder, which are now known to share a notable suite of risk alleles.

A key assumption that underlies tests of hypotheses for the relationship between autism and schizophrenia is accuracy of diagnoses. For schizophrenia, this is seldom at issue. However, diagnoses of autism, or autism spectrum disorders such as PDD-NOS, are normally made at an age well before the first manifestations of schizophrenia in adolescence or early adulthood, which generates a risk for false-positive diagnoses of premorbidity to schizophrenia as autism or autism spectrum (e.g., Eliez, 2007). The tendencies for males to exhibit worse premorbidity to schizophrenia than females (Sobin et al., 2001; Tandon et al., 2009), for CNVs to exert severe effects on diverse aspects of early neurodevelopment (Shinawi et al., 2009), and for schizophrenia of earlier onset to exhibit a higher male sex-ratio bias and a stronger tendency to be associated with CNVs rather than other causes (Remschmidt et al., 1994; Rapoport et al., 2009), all suggest a high risk for false-positive diagnoses of autistic spectrum conditions in individuals with these genomic risk factors (Feinstein and Singh, 2007; Reaven et al., 2008; Sugihara et al., 2008; Starling and Dossetor, 2009). Possible evidence of such risk comes from diagnoses of autism spectrum conditions in children with deletions at 15q11.2, 15q13.3, and 22q11.21, and duplications of 16p11.2, CNVs for which schizophrenia risk has been well established from studies of adults (Antshel et al., 2007; Stefansson et al., 2008; Weiss et al., 2008; Ben-Shachar et al., 2009; Doornbos et al., 2009; McCarthy et al., 2009). By contrast, autism-associated CNVs, such as deletions at 16p11.2 (Kumar et al., 2008), or duplications at 22q11.21 (Glessner et al., 2009; Crespi et al., 2009) have seldom also been reported in individuals diagnosed with schizophrenia, which suggests that false-positive diagnoses of schizophrenia as autism are uncommon.

Differentiating between a hypothesis of false-positive diagnoses of premorbidity to schizophrenia as autism, compared to a hypothesis of specific deletions or duplications shared between autism and schizophrenia, requires some combination of longitudinal studies, judicious use of endophenotypes, and adoption of relatively new diagnostic categories such as multiple complex developmental disorder (Sprong et al., 2008). Moreover, to the degree that such false positives are not uncommon, and autism and schizophrenia are underlain by diametric genetically based risk factors, inclusion of children premorbid for schizophrenia in studies on the genetic bases of autism will substantially dilute the probability of detecting significant results.

Ultimately, robust evaluation of alternative hypotheses for the relationship of autism with schizophrenia will require evidence from studies of common and rare SNP variants as well as CNVs, in-depth analyses of the neurodevelopmental and neuronal-function effects of different alterations to genes such as NRXN1, CNTNAP2, and SHANK3, and integrative data from diverse disciplines other than genetics, especially the neurosciences and psychology. Unless such interdisciplinary studies are deployed—in hypothesis-testing frameworks that use strong inference—we should expect to remain, as penned by García Márquez, in “permanent alternation between excitement and disappointment, doubt and revelation, to such an extreme that no one knows for certain where the limits of reality lay”—for yet another 100 years.

The Diametric Opposition of Autism and Psychosis: Support From a Study of Cognition
As has been noted previously, Crespi and Badcock’s (2008) theory that autism and schizophrenia are diametrically opposed disorders is certainly a novel and somewhat controversial one. In his recent blog on Psychology Today, Badcock states that the theory stands on two completely different foundations: one in evolution and genetics, and one in psychiatry and cognitive science (Badcock, 2010). While most of the comments posted before ours have addressed the relationship between autism and schizophrenia from a genetic perspective, coming from a psychology background, we note that it is the aspects of Crespi and Badcock’s theory that relate to cognition which have particularly caught our attention. While we can therefore contribute little to the discussion of a relationship between autism and schizophrenia from a genetic standpoint, we present the findings from our recent study (Russell-Smith et al., 2010), which provided the first test of Crespi and Badcock’s claim that autism and psychosis are at opposite ends of the cognitive spectrum.

In placing autism and psychosis at opposite ends of the cognitive spectrum, Crespi and Badcock (2008) propose that autistic and positive schizophrenia traits contrastingly affect preference for local versus global processing, with individuals with autism displaying a preference for local processing and individuals with positive schizophrenia displaying a preference for global processing. That is, these authors claim that while individuals with autism show a tendency to focus on detail or process features in their isolation, individuals with positive schizophrenia show a tendency to look at the bigger picture or process features as an integrated whole. Importantly, since Crespi and Badcock argue for a continuum stretching all the way from autism to psychosis, the same diametric pattern of cognition should be seen in individuals who display only mild variants of autistic and positive schizophrenia traits. This includes typical individuals who score highly on measures such as the Autism Spectrum Quotient (AQ; Baron-Cohen et al., 2001) and the Unusual Experiences subscale of the Oxford-Liverpool Inventory of Experiences (O-LIFE:UE; Mason et al., 2005). These are both reliable and commonly used measures which have been specifically designed to assess the levels of “autistic-like” traits and positive schizotypy traits in typical individuals. Since Crespi and Badcock actually argue their theory is best evaluated with reference to individuals with milder traits of autism and positive schizophrenia, it is with these populations that we investigated their claims.

A task often used to determine whether an individual has a preference for local over global processing is the Embedded Figures Test (EFT; Witkin et al., 1971), which requires individuals to detect hidden shapes within complex figures. As the test requires one to resist experiencing an integrated visual stimulus or gestalt in favor of seeing single elements, it is argued that a local processing style aids successful (i.e., faster) completion of this task (Bolte et al., 2007). Accordingly, from Crespi and Badcock’s (2008) theory, one would expect that relative to individuals with low levels of these traits, individuals with high levels of autistic-like traits should perform better on the EFT, while individuals with positive schizotypy traits should perform worse. To test this claim, our study obtained the AQ and O-LIFE:UE scores for 318 students completing psychology as part of a broader degree (e.g., a BSc or BA). Two pairs of groups (i.e., four groups in total), each consisting of 20 students, were then formed. One of these pairs consisted of High and Low AQ groups, which were selected such that they were separated substantially in their AQ scores but matched as closely as possible on their O-LIFE:UE scores. The other pair of groups, the High and Low O-LIFE:UE groups, were selected such that they were separated in their O-LIFE:UE scores, but matched as closely as possible on their AQ scores. The gender ratio was matched closely across the four groups.

To test the prediction that higher levels of autistic-like traits are associated with more skilled EFT performance, the High and Low AQ groups were compared in terms of their mean response time to accurately locate each of the embedded figures. Individuals in the High AQ group did display more skilled EFT performance than individuals in the Low AQ group, consistent with a greater preference for local over global processing in relation to higher levels of autistic-like traits (see also Almeida et al., 2010; Bolte and Poustka, 2007; Grinter et al., 2009; Grinter et al., 2009). We then compared EFT performance for the O-LIFE:UE groups to test the prediction that higher levels of positive schizotypy traits are associated with less skilled performance on this task. Consistent with a preference for global over local processing in relation to higher levels of positive schizotypy traits, individuals in the High O-LIFE:UE group displayed less skilled EFT performance than individuals in the Low O-LIFE:UE group. Therefore, results from both pairs of groups together provide support for Crespi and Badcock’s (2008) claim that autistic and positive schizophrenia traits are diametrically opposed with regard to their effect on local versus global processing.

However, the support our study offers for Crespi and Badcock’s (2008) theory was tempered slightly by our failure to find the expected contrasting patterns of non-verbal relative to verbal ability for our two pairs of groups. To display the expected patterns, relative to individuals with low levels of these traits, individuals with high levels of autistic-like traits should have displayed higher non-verbal ability relative to verbal ability, whereas individuals with high levels of positive schizotypy traits should have displayed lower non-verbal relative to verbal ability. While visual inspection of mean verbal and non-verbal scores for the O-LIFE:UE groups revealed a trend consistent with what would be expected based on Crespi and Badcock’s theory, none of the group differences was statistically significant. However, as we pointed out in our article, a study which offers a more complete assessment of this aspect of the theory is warranted. In particular, since the use of a student sample in our study no doubt led to a restriction in the range of IQ scores (especially with reference to verbal IQ), a test of community-based samples would be useful.

Therefore, while Crespi and Badcock’s (2008) theory has passed its first major test at the level of cognition, with our results indicating a contrasting effect of autistic-like and positive schizotypy traits with regard to preference for local versus global processing, further investigation of these authors’ theory at both the cognitive and genetic levels is required.

Bolte, S., Poustka, F. (2006). The broader cognitive phenotype of autism in parents: How specific is the tendency for local processing and executive function. Journal of Child Psychology and Psychiatry, 47, 639-645. Abstract